Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews

Abstract

Enthymemes, that are arguments with missing premises, are common in natural language text. They pose a challenge for the field of argument mining, which aims to extract arguments from such text. If we can detect whether a premise is missing in an argument, then we can either fill the missing premise from similar/related arguments, or discard such enthymemes altogether and focus on complete arguments. In this paper, we draw a connection between explicit vs. implicit opinion classification in reviews, and detecting arguments from enthymemes. For this purpose, we train a binary classifier to detect explicit vs. implicit opinions using a manually labelled dataset. Experimental results show that the proposed method can discriminate explicit opinions from implicit ones, thereby providing encouraging first step towards enthymeme detection in natural language texts.

Explaining Arguments with Background Knowledge

“…While such information can be easily filled in by the hearer, a computational system typically does not possess the knowledge that is needed to reconstruct the implied information. Especially in argumentative texts it is very common that premises are implied and omitted [4,22,43]. These arguments are called enthymemes.…”

The evolution of argumentation mining: From models to social media and emerging tools

“…The arguments extracted from online resources are not characterized as high-quality data, as often complicated reasoning process should take place in order the argument to be understood. Arguments with missing premises are called enthymemes [8,9] and take place often in informal discourse, creating a challenge for the approach that should be followed; discard the argument or try to fill the missing premise.…”

“…Both micro [93] and macro [94] analysis have the attention of the research community, whereas they have been approaches that combine them [87,88]. Another research topic that has gained the interest of the research community is the reconstruction of implicit warrants, although the existing research papers [8,9,29] do not utilize social media as source.…”

“…The two main tasks that fall under this category is the relation-based AM [51] and the enthymeme reconstruction [8]. Both tasks provide a useful insight for the structure of the argument, but instead of trying to evaluate its impact, they focus on the efficient use of small datasets [9,66] or they are designed to aid the task of stance detection [8].…”

“…The recent advances in Machine Learning (ML) in combination with the emergence of social web can enable impressive progress in different scientific fields with great impact on commercial applications. An AM system has the capacity to mine and analyse a great volume of text data through a variety of sources, providing tools for policymaking and socio-political sciences [2,3,4], software engineering [5], while it opens new horizons for the broader area of business, economics and finance, with the digital marketing being the most promising field [6,7,8,9].…”